title	is_beta
Gelly: Flink Graph API	true

Gelly is a Java Graph API for Flink. It contains a set of methods and utilities which aim to simplify the development of graph analysis applications in Flink. In Gelly, graphs can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Gelly provides methods to create, transform and modify graphs, as well as a library of graph algorithms.

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Using Gelly

Gelly is currently part of the staging Maven project. All relevant classes are located in the org.apache.flink.graph package.

Add the following dependency to your pom.xml to use Gelly.

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-gelly</artifactId>
    <version>{{site.version}}</version>
</dependency>

The remaining sections provide a description of available methods and present several examples of how to use Gelly and how to mix it with the Flink Java API. After reading this guide, you might also want to check the {% gh_link /flink-staging/flink-gelly/src/main/java/org/apache/flink/graph/example/ "Gelly examples" %}.

Graph Representation

In Gelly, a Graph is represented by a DataSet of vertices and a DataSet of edges.

The Graph nodes are represented by the Vertex type. A Vertex is defined by a unique ID and a value. Vertex IDs should implement the Comparable interface. Vertices without value can be represented by setting the value type to NullValue.

{% highlight java %} // create a new vertex with a Long ID and a String value Vertex<Long, String> v = new Vertex<Long, String>(1L, "foo");

// create a new vertex with a Long ID and no value Vertex<Long, NullValue> v = new Vertex<Long, NullValue>(1L, NullValue.getInstance()); {% endhighlight %}

The graph edges are represented by the Edge type. An Edge is defined by a source ID (the ID of the source Vertex), a target ID (the ID of the target Vertex) and an optional value. The source and target IDs should be of the same type as the Vertex IDs. Edges with no value have a NullValue value type.

{% highlight java %} Edge<Long, Double> e = new Edge<Long, Double>(1L, 2L, 0.5);

// reverse the source and target of this edge Edge<Long, Double> reversed = e.reverse();

Double weight = e.getValue(); // weight = 0.5 {% endhighlight %}

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Graph Creation

You can create a Graph in the following ways:

from a DataSet of edges and an optional DataSet of vertices:

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

DataSet<Vertex<String, Long>> vertices = ...

DataSet<Edge<String, Double>> edges = ...

Graph<String, Long, Double> graph = Graph.fromDataSet(vertices, edges, env); {% endhighlight %}

from a DataSet of Tuple3 and an optional DataSet of Tuple2. In this case, Gelly will convert each Tuple3 to an Edge, where the first field will be the source ID, the second field will be the target ID and the third field will be the edge value. Equivalently, each Tuple2 will be converted to a Vertex, where the first field will be the vertex ID and the second field will be the vertex value:

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

DataSet<Tuple2<String, Long>> vertexTuples = env.readCsvFile("path/to/vertex/input");

DataSet<Tuple3<String, String, Double>> edgeTuples = env.readCsvFile("path/to/edge/input");

Graph<String, Long, Double> graph = Graph.fromTupleDataSet(vertexTuples, edgeTuples, env); {% endhighlight %}

from a Collection of edges and an optional Collection of vertices:

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

List<Vertex<Long, Long>> vertexList = new ArrayList...

List<Edge<Long, String>> edgeList = new ArrayList...

Graph<Long, Long, String> graph = Graph.fromCollection(vertexList, edgeList, env); {% endhighlight %}

If no vertex input is provided during Graph creation, Gelly will automatically produce the Vertex DataSet from the edge input. In this case, the created vertices will have no values. Alternatively, you can provide a MapFunction as an argument to the creation method, in order to initialize the Vertex values:

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// initialize the vertex value to be equal to the vertex ID Graph<Long, Long, String> graph = Graph.fromCollection(edges, new MapFunction<Long, Long>() { public Long map(Long value) { return value; } }, env); {% endhighlight %}

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Graph Properties

Gelly includes the following methods for retrieving various Graph properties and metrics:

{% highlight java %} // get the Vertex DataSet DataSet<Vertex<K, VV>> getVertices()

// get the Edge DataSet DataSet<Edge<K, EV>> getEdges()

// get the IDs of the vertices as a DataSet DataSet getVertexIds()

// get the source-target pairs of the edge IDs as a DataSet DataSet<Tuple2<K, K>> getEdgeIds()

// get a DataSet of <vertex ID, in-degree> pairs for all vertices DataSet<Tuple2<K, Long>> inDegrees()

// get a DataSet of <vertex ID, out-degree> pairs for all vertices DataSet<Tuple2<K, Long>> outDegrees()

// get a DataSet of <vertex ID, degree> pairs for all vertices, where degree is the sum of in- and out- degrees DataSet<Tuple2<K, Long>> getDegrees()

// get the number of vertices long numberOfVertices()

// get the number of edges long numberOfEdges()

// get a DataSet of Triplets<srcVertex, trgVertex, edge> DataSet<Triplet<K, VV, EV>> getTriplets()

{% endhighlight %}

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Graph Transformations

Map: Gelly provides specialized methods for applying a map transformation on the vertex values or edge values. mapVertices and mapEdges return a new Graph, where the IDs of the vertices (or edges) remain unchanged, while the values are transformed according to the provided user-defined map function. The map functions also allow changing the type of the vertex or edge values.

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); Graph<Long, Long, Long> graph = Graph.fromDataSet(vertices, edges, env);

// increment each vertex value by one Graph<Long, Long, Long> updatedGraph = graph.mapVertices( new MapFunction<Vertex<Long, Long>, Long>() { public Long map(Vertex<Long, Long> value) { return value.getValue() + 1; } }); {% endhighlight %}

Filter: A filter transformation applies a user-defined filter function on the vertices or edges of the Graph. filterOnEdges will create a sub-graph of the original graph, keeping only the edges that satisfy the provided predicate. Note that the vertex dataset will not be modified. Respectively, filterOnVertices applies a filter on the vertices of the graph. Edges whose source and/or target do not satisfy the vertex predicate are removed from the resulting edge dataset. The subgraph method can be used to apply a filter function to the vertices and the edges at the same time.

{% highlight java %} Graph<Long, Long, Long> graph = ...

graph.subgraph( new FilterFunction<Vertex<Long, Long>>() { public boolean filter(Vertex<Long, Long> vertex) { // keep only vertices with positive values return (vertex.getValue() > 0); } }, new FilterFunction<Edge<Long, Long>>() { public boolean filter(Edge<Long, Long> edge) { // keep only edges with negative values return (edge.getValue() < 0); } }) {% endhighlight %}

Join: Gelly provides specialized methods for joining the vertex and edge datasets with other input datasets. joinWithVertices joins the vertices with a Tuple2 input data set. The join is performed using the vertex ID and the first field of the Tuple2 input as the join keys. The method returns a new Graph where the vertex values have been updated according to a provided user-defined map function. Similarly, an input dataset can be joined with the edges, using one of three methods. joinWithEdges expects an input DataSet of Tuple3 and joins on the composite key of both source and target vertex IDs. joinWithEdgesOnSource expects a DataSet of Tuple2 and joins on the source key of the edges and the first attribute of the input dataset and joinWithEdgesOnTarget expects a DataSet of Tuple2 and joins on the target key of the edges and the first attribute of the input dataset. All three methods apply a map function on the edge and the input data set values. Note that if the input dataset contains a key multiple times, all Gelly join methods will only consider the first value encountered.

{% highlight java %} Graph<Long, Double, Double> network = ...

DataSet<Tuple2<Long, Long>> vertexOutDegrees = network.outDegrees();

// assign the transition probabilities as the edge weights Graph<Long, Double, Double> networkWithWeights = network.joinWithEdgesOnSource(vertexOutDegrees, new MapFunction<Tuple2<Double, Long>, Double>() { public Double map(Tuple2<Double, Long> value) { return value.f0 / value.f1; } }); {% endhighlight %}

Reverse: the reverse() method returns a new Graph where the direction of all edges has been reversed.
Undirected: In Gelly, a Graph is always directed. Undirected graphs can be represented by adding all opposite-direction edges to a graph. For this purpose, Gelly provides the getUndirected() method.
Union: Gelly's union() method performs a union on the vertex and edges sets of the input graphs. Duplicate vertices are removed from the resulting Graph, while if duplicate edges exists, these will be maintained.

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Graph Mutations

Gelly includes the following methods for adding and removing vertices and edges from an input Graph:

{% highlight java %} // adds a Vertex and the given edges to the Graph. If the Vertex already exists, it will not be added again, but the given edges will. Graph<K, VV, EV> addVertex(final Vertex<K, VV> vertex, List<Edge<K, EV>> edges)

// adds an Edge to the Graph. If the source and target vertices do not exist in the graph, they will also be added. Graph<K, VV, EV> addEdge(Vertex<K, VV> source, Vertex<K, VV> target, EV edgeValue)

// removes the given Vertex and its edges from the Graph. Graph<K, VV, EV> removeVertex(Vertex<K, VV> vertex)

// removes all edges that match the given Edge from the Graph. Graph<K, VV, EV> removeEdge(Edge<K, EV> edge) {% endhighlight %}

Neighborhood Methods

Neighborhood methods allow vertices to perform an aggregation on their first-hop neighborhood. reduceOnEdges() can be used to compute an aggregation on the values of the neighboring edges of a vertex and reduceOnNeighbors() can be used to compute an aggregation on the values of the neighboring vertices. These methods assume associative and commutative aggregations and exploit combiners internally, significantly improving performance. The neighborhood scope is defined by the EdgeDirection parameter, which takes the values IN, OUT or ALL. IN will gather all in-coming edges (neighbors) of a vertex, OUT will gather all out-going edges (neighbors), while ALL will gather all edges (neighbors).

For example, assume that you want to select the minimum weight of all out-edges for each vertex in the following graph:

The following code will collect the out-edges for each vertex and apply the SelectMinWeight() user-defined function on each of the resulting neighborhoods:

{% highlight java %} Graph<Long, Long, Double> graph = ...

DataSet<Tuple2<Long, Double>> minWeights = graph.reduceOnEdges(new SelectMinWeight(), EdgeDirection.OUT);

// user-defined function to select the minimum weight static final class SelectMinWeight implements ReduceEdgesFunction {

	@Override
	public Double reduceEdges(Double firstEdgeValue, Double secondEdgeValue) {
		return Math.min(firstEdgeValue, secondEdgeValue);
	}

} {% endhighlight %}

Similarly, assume that you would like to compute the sum of the values of all in-coming neighbors, for every vertex. The following code will collect the in-coming neighbors for each vertex and apply the SumValues() user-defined function on each neighborhood:

{% highlight java %} Graph<Long, Long, Double> graph = ...

DataSet<Tuple2<Long, Long>> verticesWithSum = graph.reduceOnNeighbors(new SumValues(), EdgeDirection.IN);

// user-defined function to sum the neighbor values static final class SumValues implements ReduceNeighborsFunction {

    	@Override
    	public Long reduceNeighbors(Long firstNeighbor, Long secondNeighbor) {
	    	return firstNeighbor + secondNeighbor;
  	}

} {% endhighlight %}

When the aggregation function is not associative and commutative or when it is desirable to return more than one values per vertex, one can use the more general groupReduceOnEdges() and groupReduceOnNeighbors() methods. These methods return zero, one or more values per vertex and provide access to the whole neighborhood.

For example, the following code will output all the vertex pairs which are connected with an edge having a weight of 0.5 or more:

{% highlight java %} Graph<Long, Long, Double> graph = ...

DataSet<Tuple2<Long, Long>> vertexPairs = graph.groupReduceOnNeighbors(new SelectLargeWeightNeighbors(), EdgeDirection.OUT);

// user-defined function to select the neighbors which have edges with weight > 0.5 static final class SelectLargeWeightNeighbors implements NeighborsFunctionWithVertexValue<Long, Long, Double, Tuple2<Vertex<Long, Long>, Vertex<Long, Long>>> {

	@Override
	public void iterateNeighbors(Vertex<Long, Long> vertex,
			Iterable<Tuple2<Edge<Long, Double>, Vertex<Long, Long>>> neighbors,
			Collector<Tuple2<Vertex<Long, Long>, Vertex<Long, Long>>> out) {

		for (Tuple2<Edge<Long, Double>, Vertex<Long, Long>> neighbor : neighbors) {
			if (neighbor.f0.f2 > 0.5) {
				out.collect(new Tuple2<Vertex<Long, Long>, Vertex<Long, Long>>(vertex, neighbor.f1));
			}
		}
	}

} {% endhighlight %}

When the aggregation computation does not require access to the vertex value (for which the aggregation is performed), it is advised to use the more efficient EdgesFunction and NeighborsFunction for the user-defined functions. When access to the vertex value is required, one should use EdgesFunctionWithVertexValue and NeighborsFunctionWithVertexValue instead.

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Vertex-centric Iterations

Gelly wraps Flink's Spargel API to provide methods for vertex-centric iterations. Like in Spargel, the user only needs to implement two functions: a VertexUpdateFunction, which defines how a vertex will update its value based on the received messages and a MessagingFunction, which allows a vertex to send out messages for the next superstep. These functions and the maximum number of iterations to run are given as parameters to Gelly's runVertexCentricIteration. This method will execute the vertex-centric iteration on the input Graph and return a new Graph, with updated vertex values:

{% highlight java %} Graph<Long, Double, Double> graph = ...

// run Single-Source-Shortest-Paths vertex-centric iteration Graph<Long, Double, Double> result = graph.runVertexCentricIteration( new VertexDistanceUpdater(), new MinDistanceMessenger(), maxIterations);

// user-defined functions public static final class VertexDistanceUpdater {...} public static final class MinDistanceMessenger {...}

{% endhighlight %}

Configuring a Vertex-Centric Iteration

A vertex-centric iteration can be configured using an IterationConfiguration object. Currently, the following parameters can be specified:

Name: The name for the vertex-centric iteration. The name is displayed in logs and messages and can be specified using the setName() method.
Parallelism: The parallelism for the iteration. It can be set using the setParallelism() method.
Solution set in unmanaged memory: Defines whether the solution set is kept in managed memory (Flink's internal way of keeping objects in serialized form) or as a simple object map. By default, the solution set runs in managed memory. This property can be set using the setSolutionSetUnmanagedMemory() method.
Aggregators: Iteration aggregators can be registered using the registerAggregator() method. An iteration aggregator combines all aggregates globally once per superstep and makes them available in the next superstep. Registered aggregators can be accessed inside the user-defined VertexUpdateFunction and MessagingFunction.
Broadcast Variables: DataSets can be added as Broadcast Variables to the VertexUpdateFunction and MessagingFunction, using the addBroadcastSetForUpdateFunction() and addBroadcastSetForMessagingFunction() methods, respectively.

{% highlight java %}

Graph<Long, Double, Double> graph = ...

// configure the iteration IterationConfiguration parameters = new IterationConfiguration();

// set the iteration name parameters.setName("Gelly Iteration");

// set the parallelism parameters.setParallelism(16);

// register an aggregator parameters.registerAggregator("sumAggregator", new LongSumAggregator());

// run the vertex-centric iteration, also passing the configuration parameters Graph<Long, Double, Double> result = graph.runVertexCentricIteration( new VertexUpdater(), new Messenger(), maxIterations, parameters);

// user-defined functions public static final class VertexUpdater extends VertexUpdateFunction {

LongSumAggregator aggregator = new LongSumAggregator();

public void preSuperstep() {

	// retrieve the Aggregator
	aggregator = getIterationAggregator("sumAggregator");
}


public void updateVertex(Long vertexKey, Long vertexValue, MessageIterator inMessages) {
	
	//do some computation
	Long partialValue = ...

	// aggregate the partial value
	aggregator.aggregate(partialValue);

	// update the vertex value
	setNewVertexValue(...);
}

}

public static final class Messenger extends MessagingFunction {...}

{% endhighlight %}

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Graph Validation

Gelly provides a simple utility for performing validation checks on input graphs. Depending on the application context, a graph may or may not be valid according to certain criteria. For example, a user might need to validate whether their graph contains duplicate edges or whether its structure is bipartite. In order to validate a graph, one can define a custom GraphValidator and implement its validate() method. InvalidVertexIdsValidator is Gelly's pre-defined validator. It checks that the edge set contains valid vertex IDs, i.e. that all edge IDs also exist in the vertex IDs set.

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

// create a list of vertices with IDs = {1, 2, 3, 4, 5} List<Vertex<Long, Long>> vertices = ...

// create a list of edges with IDs = {(1, 2) (1, 3), (2, 4), (5, 6)} List<Edge<Long, Long>> edges = ...

Graph<Long, Long, Long> graph = Graph.fromCollection(vertices, edges, env);

// will return false: 6 is an invalid ID graph.validate(new InvalidVertexIdsValidator<Long, Long, Long>());

{% endhighlight %}

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Library Methods

Gelly has a growing collection of graph algorithms for easily analyzing large-scale Graphs. So far, the following library methods are implemented:

PageRank
Single-Source Shortest Paths
Label Propagation
Simple Community Detection
Connected Components

Gelly's library methods can be used by simply calling the run() method on the input graph:

{% highlight java %} ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

Graph<Long, Long, NullValue> graph = ...

// run Label Propagation for 30 iterations to detect communities on the input graph DataSet<Vertex<Long, Long>> verticesWithCommunity = graph.run( new LabelPropagation(30)).getVertices();

// print the result verticesWithCommunity.print();

env.execute(); {% endhighlight %}

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gelly_guide.md

gelly_guide.md

Using Gelly

Graph Representation

Graph Creation

Graph Properties

Graph Transformations

Graph Mutations

Neighborhood Methods

Vertex-centric Iterations

Configuring a Vertex-Centric Iteration

Graph Validation

Library Methods

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Using Gelly

Graph Representation

Graph Creation

Graph Properties

Graph Transformations

Graph Mutations

Neighborhood Methods

Vertex-centric Iterations

Configuring a Vertex-Centric Iteration

Graph Validation

Library Methods